SQL vs Python for Data Analysis

The data industry has seen a shift from transforming data in memory with programming languages like Python and Java, using tools like Hadoop, Spark, and Dask, back to transforming data within warehouses. This change is largely driven by dbt (data build tool), which has fixed important limitations of SQL and is showing strong adoption. The clean division of labor between SQL (data querying and consolidation) and Python (complex data transformation) is fading as tools like dask-sql allow you to both query and transform data using a mix of SQL operations and Python code. While SQL may often be faster than Python for basic queries and aggregations, it does not have the same range of functionality. The developer experience with Python is also generally better due to its support for testing, debugging, and code version control. However, tools are emerging that recognize the advantages of each language and bridge the gap between them, allowing data professionals to use SQL for efficient querying and aggregating, dbt for organizing complex SQL models, and Python with distributed computing libraries like Dask for exploratory analysis and machine learning code.